Name: 【TED】傑瑞米.霍華德: 有學習能力的電腦帶來的美好但駭人影響 (The wonderful and terrifying implications of computers that can learn | Jeremy Howard)
Uploaded: 2015-07-06T16:00:10.000Z
Duration: 19 min 50 s

過去簡單式

It used to be that if you wanted to get a computer to do something new,

過去如果想用電腦來作點新東西，

情態助動詞 A2

Now, programming, for those of you here that haven't done it yourself,

而現在，你們可能沒做過程式設計這件事，

requires laying out in excruciating detail

every single step that you want the computer to achieve, to do

那些你想讓電腦執行的每一個步驟

Now, if you want to do something that you don't know how to do yourself,

then this is going to be a great challenge.

So this was the challenge faced by this man, Arthur Samuel.

亞瑟·撒姆爾也曾面對這種挑戰。

lay out in excruciating detail, how to be better than you at checkers?

把細節通通寫出來，如何讓電腦比你還會下棋？

he had the computer play against itself thousands of times

他讓電腦與電腦本身對弈數千次

And indeed it worked, and in fact, by 1962,

this computer had beaten the Connecticut state champion.

電腦打敗了康乃狄克州的冠軍。

So Arthur Samuel was the father of machine learning,

於是亞瑟·撒姆爾成為了機器學習之父，

because I am a machine learning practitioner.

因為我也是個機器學習實踐者，

a community of over 200,000 machine learning practitioners.

Kaggle 是個超過 20 萬人的機器學習實踐者的社群。

to try and get them to solve previously unsolved problems,

讓他們參與解決過去無法解決的問題，

and it's been successful  hundreds of times.

So from this vantage point, I was able to find out

a lot about what machine learning can do in the past, can do today,

很多機器學習在過去和現在可以做到的事情，

Perhaps the first big success of  machine learning commercially was Google.

第一個機器學習的商業成功案例是谷歌。

Google showed that it is possible to find information

and this algorithm is based on machine learning.

而這演算法是以機器學習為基礎。

Since that time, there have been many commercial successes of machine learning.

自此，機器學習有很多的商業成功例子，

use machine learning to suggest products that you might like to buy,

用機器學習會向你推薦你可能想買的商品，

sometimes will tell you about who your friends might be

有些時候會告訴你誰會是你的朋友

而你根本不知道他們是如何做到的，

and this is because it's using the power of machine learning.

因為他們用了機器學習這強大的功能。

These are algorithms that have learned how to do this from data

in getting Watson to beat the two world champions at "Jeopardy,"

讓超級電腦「華生」在「危機遊戲」中打敗兩屆世界冠軍。

answering incredibly subtle and complex questions like this one.

回答一些細碎和複雜的問題，像是

「2003年，古獅像在這城市的國家博物館消失了（連同其他物品）」

This is also why we are now able to see the first self-driving cars.

這也是我們現在能看到第一部自行駕駛汽車的原因。

If you want to be able to tell the difference between, say,

a tree and a pedestrian, well, that's pretty important.

一棵樹和一條行人道，那顯得非常重要。

We don't know how to write those programs by hand,

我們不知道如何設計這樣的程式，

but with machine learning, this is now possible.

不過通過機器，這就成為可能。

And in fact, this car has driven  over a million miles

事實上，這部汽車已經行駛一百萬英哩

我們現在都知道電腦能夠學習，

that we actually sometimes don't know how to do ourselves,

有時我們自己也不知道怎麼做的事，

One of the most amazing examples I've seen of machine learning

happened on a project that I ran at Kaggle

where a team run by a guy called Geoffrey Hinton

won a competition for automatic drug discovery.

Now, what was extraordinary here is not just that they beat

all of the algorithms developed by Merck or the international academic community,

默克藥廠或國際學術社群所研發的演算法，

but nobody on the team had any background in chemistry or biology or life sciences,

他們的團隊沒有化學生物或生命科學的背景，

They used an extraordinary algorithm called deep learning.

他們用了一個很出色的演算法叫做「深度學習」。

So important was this that in fact the success was covered

in The New York Times in a front page article a few weeks later.

在數星期後被刊登在紐約時報頭版。

This is Geoffrey Hinton here on the left-hand side.

Deep learning is an algorithm inspired by how the human brain works,

深度學習是一種受到人類大腦啟發的演算法，

which has no theoretical limitations on what it can do.

The more data you give it and the more computation time you give it,

你給它越多的資料和運算時間，

The New York Times also showed in this article

another extraordinary result of deep learning

It shows that computers  can listen and understand.

它顯示電腦能聽懂和理解資料的能力。

(Video) Richard Rashid: Now, the last step

（影片）理察·拉希德: 現在，最後一步是

that I want to be able to take in this process

we've been able to take a large amount  of information from many Chinese speakers

我們從很多講中文的人士中收集大量的資訊

that takes Chinese text and converts it into Chinese language,

and then we've taken an hour or so of my own voice

the standard text-to-speech system so that it would sound like me.

使標準文字轉化語音系統的聲音聽起來像我的聲音。

再一次，雖然結果沒有很完美，

There's much work to be done in this area.

在這個領域還有很多工作要做。

Jeremy Howard: Well, that was at a machine learning conference in China.

傑里米·霍華德：那是在中國舉行的機器學習研討會。

字幕列表影片播放

【TED】傑瑞米.霍華德: 有學習能力的電腦帶來的美好但駭人影響 (The wonderful and terrifying implications of computers that can learn | Jeremy Howard)

people

learn

system

similar